关键词: 故障选线, 格拉姆角差场, 卷积注意力机制, 滑动窗口变换器, 特征提取

Abstract:

Due to the weak fault characteristics when a single-phase-to-ground fault occurs in the small current system of distribution networks，existing fault line selection methods often suffer from low accuracy and weak robustness. Therefore，a fault line selection method for distribution networks based on BMF-GADF and an improved Swin Transformer was proposed. The zero-sequence current was converted into a feature-enhanced Gramian angular difference field image by combining Butterworth mean filtering and Gramian angular difference field. The image samples were then input into the improved Swin Transformer model for feature extraction. Building upon the original architecture，the improved Swin Transformer innovatively introduced a parallel convolution block attention module，enabling more accurate adaptive feature selection and effectively enhancing model accuracy. The line selection results were output through Softmax. The experimental results showed that the fault line selection accuracy of the proposed method reached 98.96%. Compared with other fault line selection methods，the proposed method demonstrated higher line selection accuracy and better noise robustness，offering a new solution for fault line selection in distribution networks.

Key words: fault line selection, Gramian angular difference field, convolution block attention module, Swin Transformer, feature extraction